A Machine Learning Approach to Generate Rules for Process Fault Diagnosis

• Shastri Srinivas

  School of Engineering, Murdoch University

• Lam Chiou-Peng

  School of Computer and Information Science, Edith Cowan University

• Werner Brenda

  School of Engineering, Murdoch University

Bibliographic Information

Other Title

• Machine Learning Approach to Generate Rules for Process Fault Diagnosis

Abstract

Expert systems can play a very important role in manufacturing processes by locating problems as soon as they arise. The most important ingredient in any expert system is knowledge. The current knowledge acquisition method is slow and tedious and there exist substantial difficulties in acquiring the knowledge for complex processes. An approach is proposed that makes use of the machine learning technique, C4.5, to generate a decision tree. The decision tree is translated into rules that are implemented into the expert system shell, G2. The rules are tested using a sensitivity analysis of the system. The approach works well, but depends on both the quality and quantity of available training data.

Journal

• JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

  JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 37 (6), 691-697, 2004

  The Society of Chemical Engineers, Japan

Keywords

• Fault Diagnosis
• Expert Systems
• Models

Details

• CRID: 1390001204568039168
• NII Article ID: 10013340627
• NII Book ID: AA00709658
• DOI: 10.1252/jcej.37.691
• COI: 1:CAS:528:DC%2BD2cXlsVOntLc%3D
• ISSN: 18811299
• ISSN: 00219592
• NDL BIB ID: 6981492
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I6981492
• Web Site: http://www.jstage.jst.go.jp/article/jcej/37/6/37_6_691/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed